{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720745909845e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularization loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.7963408538435033e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 3.561318e-09\n",
      "W2 max relative error: 3.440708e-09\n",
      "b1 max relative error: 1.555471e-09\n",
      "b2 max relative error: 1.276034e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.017149607938732093\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "245.0\n",
      "iteration 0 / 1000: loss 2.302970\n",
      "iteration 100 / 1000: loss 2.302474\n",
      "iteration 200 / 1000: loss 2.297076\n",
      "iteration 300 / 1000: loss 2.257328\n",
      "iteration 400 / 1000: loss 2.230484\n",
      "iteration 500 / 1000: loss 2.150620\n",
      "iteration 600 / 1000: loss 2.080736\n",
      "iteration 700 / 1000: loss 2.054914\n",
      "iteration 800 / 1000: loss 1.979290\n",
      "iteration 900 / 1000: loss 2.039101\n",
      "Validation accuracy:  0.287\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Explain your hyperparameter tuning process below.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy:  0.468\n",
      "Validation accuracy:  0.499\n",
      "Validation accuracy:  0.453\n",
      "Validation accuracy:  0.484\n",
      "lr 2.000000e-03 rs 5.000000e-01 hs 1.000000e+02 de 7.500000e-01 bs 3.000000e+02 val accuracy: 0.453000\n",
      "lr 2.000000e-03 rs 5.000000e-01 hs 1.000000e+02 de 7.500000e-01 bs 4.000000e+02 val accuracy: 0.484000\n",
      "lr 2.000000e-03 rs 5.000000e-01 hs 1.000000e+02 de 8.500000e-01 bs 3.000000e+02 val accuracy: 0.468000\n",
      "lr 2.000000e-03 rs 5.000000e-01 hs 1.000000e+02 de 8.500000e-01 bs 4.000000e+02 val accuracy: 0.499000\n",
      "best validation accuracy achieved during cross-validation: 0.499000\n"
     ]
    }
   ],
   "source": [
    "best_net = None # store the best model into this \n",
    "\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "\n",
    "input_size = 32 * 32 * 3\n",
    "num_classes = 10\n",
    "\n",
    "# learning_rates = [1e-4, 5e-4, 1e-3]\n",
    "# regularization_strengths = [0.25, 0.5, 0.75]\n",
    "# hidden_size = [50, 75, 100]\n",
    "# decay = [0.95, 0.85, 0.75]\n",
    "# batch_size = [200, 300, 400]\n",
    "learning_rates = [2e-3]\n",
    "regularization_strengths = [0.5]\n",
    "hidden_size = [100]\n",
    "decay = [0.85, 0.75]\n",
    "batch_size = [300, 400]\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        for hs in hidden_size:\n",
    "            for de in decay:\n",
    "                for bs in batch_size:\n",
    "                    net = TwoLayerNet(input_size, hs, num_classes)\n",
    "\n",
    "                    # Train the network\n",
    "                    stats = net.train(X_train, y_train, X_val, y_val,\n",
    "                            num_iters=1000, batch_size=bs,\n",
    "                            learning_rate=lr, learning_rate_decay=de,\n",
    "                            reg=rs, verbose=False)\n",
    "                    \n",
    "                     # Predict on the validation set\n",
    "                    val_acc = (net.predict(X_val) == y_val).mean()\n",
    "                    print('Validation accuracy: ', val_acc)\n",
    "                    \n",
    "                    results[(lr, rs, hs, de, bs)] = (val_acc)\n",
    "                    if best_val < val_acc:\n",
    "                        best_val = val_acc\n",
    "                        best_net = net\n",
    "\n",
    "# Print out results.\n",
    "for lr, rs, hs, de, bs in sorted(results):\n",
    "    val_accuracy = results[(lr, rs, hs, de, bs)]\n",
    "    print('lr %e rs %e hs %e de %e bs %e val accuracy: %f' % (\n",
    "                lr, rs, hs, de, bs, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.513\n"
     ]
    }
   ],
   "source": [
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "1\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "训练精度和测试精度有误差一般是因为训练样本的代表性不高，即模型的泛化能力差，可以通过增加训练样本的种类和规模改善模型的泛化能力。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
